Abstract
This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning’ of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information’, which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information’, but also ‘local context information’ for extracting keywords from documents. As far as we know, utilizing both ‘global context information’ and ‘local context information’ in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Azcarraga, A., Yap, T.J., Chua, T.S.: Comparing Keyword Extraction Techniques for WEBSOM Text Archives. International Journal of Artificial Intelligence Tools 11(2), 219–232 (2000)
Berger, A.L., Mittal, V.O.: OCELOT: A System for Summarizing Web Pages. In: Proceedings of the 23rd ACM SIGIR Conference, pp. 144–151 (2000)
Brill, E., Ngai, G.: Man vs. machine: A case study in baseNP learning. In: Proceedings of the 18th International Conference on Computational Linguistics, pp. 65–72 (1999)
Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics, Philadelphia (2002)
Document Understanding Conference, http://www-nlpir.nist.gov/projects/duc/
Dumais, S.T., Platt, J., Heckerman, D., Sahami, M.: Inductive Learning Algorithms and Representations for Text Categorization. In: Proceedings of the 7th International Conference on Information and Knowledge Management, pp. 148–155 (1998)
Frank, E., Paynter, G.W., Witten, I.H.: Domain-Specific Keyphrase Extraction. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 668–673. Morgan Kaufmann, San Francisco (1999)
Hulth, A.: Combining Machine Learning and Natural Language Processing for Automatic Keyword Extraction. Ph.D. diss., Dept. of Computer and Systems Sciences, Stockholm University (2004)
Mani, I., Maybury, M.T.: Advances in Automatic Text Summarization. The MIT Press, Cambridge (1999)
Mani, I.: Automatic Summarization. John Benjamins Pub.Co., Amsterdam (2001)
Matsuo, Y., Ishizuka, M.: Keyword Extraction from a Single Document using Word Co-occurrence Statistical Information. Int’l Journal on Artificial Intelligence Tools 13(1), 157–169 (2004)
Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Wordnet: An On-line Lexical Database. International Journal of Lexicography 3(4), 235–312 (1990)
Sleator, D., Temperley, D.: Parsing English with a Link Grammar. Technical Report, CMU-CS-91-196, Dept. of Computer Science, Carnegie Mellon University (1991)
Tang, J., Li, J.Z., Wang, K.H., Cai, Y.R.: Loss Minimization based Keyword Distillation. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 572–577. Springer, Heidelberg (2004)
Turney, P.D.: Learning Algorithms for Keyphrase Extraction. Information Retrieval 2(4), 303–336 (2000)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Witten, I.H., Paynter, G.W., et al.: KEA: Practical Automatic Keyphrase Extraction. In: Proceedings of 4th ACM Conference on Digital Libraries, Berkeley, CA, pp. 254–255 (1999)
Xun, E., Huang, C., Zhou, M.: A Unified Statistical Model for the Identification of English baseNP. In: Proceedings of the 38th Annual Meeting of the Association for ComputationalLinguistics, Hong Kong (2000)
Zha, H.: Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering. In: Proceedings of the 25th ACM SIGIR Conference, pp. 113–120 (2002)
Zhu, M., Cai, Z., Cai, Q.: Automatic Keywords Extraction of Chinese Document Using Small World Structure. In: Proceeding of the international conference on Natural Language Processing and Knowledge Engineering (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, K., Xu, H., Tang, J., Li, J. (2006). Keyword Extraction Using Support Vector Machine. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_8
Download citation
DOI: https://doi.org/10.1007/11775300_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35225-9
Online ISBN: 978-3-540-35226-6
eBook Packages: Computer ScienceComputer Science (R0)